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Learning Apache Apex

Learning Apache Apex

By : Gundabattula, Thomas Weise, Munagala V. Ramanath, David Yan, Kenneth Knowles
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Learning Apache Apex

Learning Apache Apex

5 (1)
By: Gundabattula, Thomas Weise, Munagala V. Ramanath, David Yan, Kenneth Knowles

Overview of this book

Apache Apex is a next-generation stream processing framework designed to operate on data at large scale, with minimum latency, maximum reliability, and strict correctness guarantees. Half of the book consists of Apex applications, showing you key aspects of data processing pipelines such as connectors for sources and sinks, and common data transformations. The other half of the book is evenly split into explaining the Apex framework, and tuning, testing, and scaling Apex applications. Much of our economic world depends on growing streams of data, such as social media feeds, financial records, data from mobile devices, sensors and machines (the Internet of Things - IoT). The projects in the book show how to process such streams to gain valuable, timely, and actionable insights. Traditional use cases, such as ETL, that currently consume a significant chunk of data engineering resources are also covered. The final chapter shows you future possibilities emerging in the streaming space, and how Apache Apex can contribute to it.
Table of Contents (11 chapters)
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Serving the data with WebSocket


We have just finished the aggregation of real-time ride data, and now we have the dollar amount for each zip code with sliding data in real time. We have to make use of this data in real time as well. Let's do that!

NycTaxiDataServer is an operator that listens to the triggers from the aforementioned WindowedOperator. It also listens for incoming query messages via WebSocket, processes the queries according to the real-time state, and sends back the results, again via WebSocket.

In order to do that, NycTaxiDataServer extends from the AbstractAppDataServer class, which provides the embedded query listening capability. This allows an input operator to be embedded in the operator so that message from the input operator can be sent immediately to the operator. If the input operator is part of the pipeline, the messages from the input operator could be delayed due to lag of the rest of the pipeline.

Note that the triggers from the upstream WindowedOperator are sent...

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